A novel scheme to assess factors involved in the reproducibility of DNA-microarray data

Background  

In research laboratories using DNA-microarrays, usually a number of researchers perform experiments, each generating possible sources of error. There is a need for a quick and robust method to assess data quality and sources of errors in DNA-microarray experiments. To this end, a novel and cost-effective validation scheme was devised, implemented, and employed.     

Vegetal cover estimated by digital photographs related to biomass in a grassland site in northern Mexico

A regression model was used to determine the relationship between aerial herbaceous biomass and vegetation coverage estimated by digital images. Four samplings (n=36 each date) of vegetation cover and herbaceous biomass were performed during the growing season in 2011 in a grassland dominated by Bouteloua gracilis in La Cieneguilla, Municipality of Villa Hidalgo, Durango. Average production of dry biomass was 37.36 ± 9.66 g/m², and mean vegetation cover 30.02%. Dry biomass data were tested for normality using the test of Kolmogorov Smirnov, finding a lack of fit. The data were subjected to a logarithmic transformation and the model Ln(y) = 1.637926 + 0.08501X - 0.000586X² with an adjusted R² = 0.89 was found. In order to validate this model, another five samplings were carried out in 2013 at the same site during summer and autumn, using the same sampling size for each date as in 2011. Data collected in 2013 were analyzed with the model Ln (y) = β₀ + β₁X + β₂X². A comparison of regression coefficients was carried out between the 2011 and 2013 models with t (180+144-9-11-2=302, p<0.05) = 1.967. The results indicated that it is possible to use the 2011 regression model to estimate herbaceous aerial biomass from vegetation cover measurements with aerial photographs in La Cieneguilla site during summer and fall.